PTPPI: A Study on Protein Inhibitor Prediction Methods Using Multimodal Feature Fusion and Attention Mechanism

Protein-protein interactions (PPIs) are fundamental to many biological processes, including cell signaling, gene expression regulation, immune responses, and protein complex formation. Small molecule inhibitors targeting specific PPIs are expected to treat diseases such as cancer and viral infections by modulating pathophysiological processes. Despite their clinical importance, the development of PPI inhibitors is challenging due to limited experimental validation data, which complicates the accurate prediction of novel inhibitors. Therefore, there is an urgent need for advanced computational methods that can effectively integrate multiple data types and improve prediction accuracy. In this study, we proposed a new framework, PTPPI, to efficiently predict protein-protein interaction inhibitors (PPIIs). PTPPI integrates multiple molecular features, including extended connectivity fingerprints (ECFPs) for structural representation and deep semantic embeddings of SMILES sequences generated by the ChemBERTa pre-trained model. These features are processed by independent encoders and fused using an interactive attention mechanism, which enhances the molecular representation. In addition, PTPPI adopts a multi-task learning approach, enabling the model to both reconstruct input features and accurately predict inhibition scores. Experimental results on eight PPI target families, focusing on inhibitor identification and potency prediction, demonstrate that PTPPI outperforms existing methods. It not only integrates multiple molecular features effectively but also achieves superior prediction performance. This makes PTPPI a valuable and reliable tool for discovering new PPI inhibitors, thus opening up new possibilities for drug discovery and disease treatment.